This paper presents a regularized fuzzy c-means clustering method for brain tissue segmentation from magnetic resonance images. A regularizer of the total variation type is explored and a method to estimate the regularization parameter is proposed. 2007 Elsevier B.V. All rights reserved.

MOTIVATION Clustering analysis of data from DNA microarray hybridization studies is essential for identifying biologically relevant groups of genes. Partitional clustering methods such as K-means or self-organizing maps assign each gene to a single cluster. However, these methods do not provide information about the influence of a given gene for the overall shape of clusters. Here we apply a fu...

The kernelized fuzzy c-means algorithm uses kernel methods to improve the clustering performance of the well known fuzzy c-means algorithm by mapping a given dataset into a higher dimensional space non-linearly. Thus, the newly obtained dataset is more likely to be linearly seprable. However, to further improve the clustering performance, an optimization method is required to overcome the drawb...

We introduce a new clustering method for DNA microarray data that is based on space filling curves and wavelet denoising. The proposed method is much faster than the established fuzzy c-means clustering because clustering occurs in one dimension and it clusters cells that contain data, instead of data themselves. Moreover, preliminary evaluation results on data sets from Small Round Blue-Cell t...

Fuzzy C-means (FCM) is an important clustering algorithm with broad applications such as retail market data analysis, network monitoring, web usage mining, and stock prediction. Especially, parameters in FCM have influence on results. However, a lot of did not solve the problem, that is, how to set parameters. In this study, we present kind method for computing values according role process. Ne...

In fuzzy clustering, the fuzzy c-means (FCM) algorithm is the most commonly used clustering method. However, the FCM algorithm is usually affected by initializations. Incorporating FCM into switching regressions, called the fuzzy c-regressions (FCR), has also the same drawback as FCM, where bad initializations may cause difficulties in obtaining appropriate clustering and regression results. In...

Interpretation of MRI images is difficult due to inherent noise and inhomogeneity. Segmentation is considered as vitally important step in medical image analysis and classification. Several methods are employed for medical image segmentation such as clustering method, thresholding method, region growing etc. In this paper, attention has been focused on clustering method such as Fuzzy C-means cl...